Este articulo parte de la carencia, en la literatura especializada, de consenso y unanimidad en torno a la conceptualización y caracterización de aquellas agrupaciones políticas; partidos o movimientos, que no obedecen a los partidos políticos tradicionales, y por lo tanto, el articulo propone brindar aporte en dicha dirección. Tales expresiones políticas son resultado de la crisis de representación y de los partidos políticos que inició en América Latina desde la década de los 80 del siglo xx. Existen diferencias entre ellas, según las particularidades decada país y sistema electoral. También, elementos en común: son el resultado de procesos democráticos plasmados en la constitución política de cada Estado, y expresión de inconformismo social, o como señala Tilly (2009) movimiento implica la noción y condición de acción colectiva a algo que no permanece estático, su dinamismo se debe a las demandas del grupo en cuestión. Estas demandas son las que componen una serie de afinidades, intereses y aspiraciones que en el movimiento toman carácter de colectivas y no de individuales.

Well-being and happiness are increasingly recognized as key outcomes in higher education, yet few Latin American public universities systematically assess them at the institutional level. This study reports the results of an institutional measurement of well-being and happiness, based on the SPIRE model (Spiritual, Physical, Intellectual, Relational, and Emotional) and NPS, applied to the entire academic community. The database was collected through a digital survey grounded in the SPIRE dimensions, administered to students, faculty, staff, and contractors at a Colombian public university. A total of 877 complete responses were obtained from a population of 8362 individuals. Data were analyzed using descriptive and inferential statistics, psychometric analysis, clustering, and supervised classification techniques. The findings reveal differences in well-being across institutional roles, with faculty and staff reporting higher levels of overall well-being than students, particularly in the physical and emotional dimensions. The analysis also highlights patterns in perceived life purpose, interpersonal relationships, and intellectual well-being. The Random Forest classifier achieved an accuracy of 95.8% in reproducing membership in data-driven well-being clusters derived from the SPIRE dimensions. This classification performance reflects the reproducibility of the cluster structure within the same institutional dataset and should not be interpreted as evidence of future well-being prediction or independent predictive validity.

Electroencephalographic (EEG) signals have been extensively studied for emotion detection and, more recently, as an alternative for biometric identification and authentication. Biometric methods based on physiological signals are a non-conventional approach for personal identification, and their study is currently considered an open research field. However, EEG-based biometric systems face several challenges, including the influence of emotional states, which can affect their performance. This study evaluates the effect of emotional states on the performance of an EEG-based biometric system. Four widely used databases for biometrics and emotion recognition (DEAP, MAHNOB, SEED, and LUMED-2) were selected for analysis. Feature extraction was performed using multiple strategies in the time, frequency, and time–frequency domains. The performance of various classifiers—support vector machine (SVM), random forest (RF), artificial neural networks (ANN), and k-nearest neighbors (K-NN)—was evaluated separately. Furthermore, stacking was used as a classifier fusion method. Explicit modeling of emotional states contributed to improving classifier performance. The best model based on classifier fusion achieved an accuracy of 95.73 ± 1.83%. These results indicate that incorporating information about emotional state into EEG-based biometric systems can contribute to the development of more robust and realistic identification solutions.